Multi-modal graph neural network for early diagnosis of Alzheimer's disease from sMRI and PET scans
In recent years, deep learning models have been applied to neuroimaging data for early
diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and …
diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and …
Concordance between logical memory and craft story 21 in community-dwelling older adults: the role of demographic factors and cognitive status
Objective Episodic memory loss, a hallmark symptom of Alzheimer's Disease, is frequently
quantified by story memory performance. The National Alzheimer's Coordinating Center …
quantified by story memory performance. The National Alzheimer's Coordinating Center …
Examining the relationship between brain activation and proxies of disease severity using quantile regression in individuals at risk of Alzheimer's disease
L Décarie-Labbé, IZ Dialahy, N Corriveau-Lecavalier… - Cortex, 2024 - Elsevier
Previous studies have reported a pattern of hyperactivation in the pre-dementia phase of
Alzheimer's disease (AD), followed by hypoactivation in later stages of the disease. This …
Alzheimer's disease (AD), followed by hypoactivation in later stages of the disease. This …
A joint CNN-GNN framework for early diagnosis of AD using multi-source multi-modal data
Y Zhang, Q Cai, X He, X Ren… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
With the abundance of medical data, computer-aided AD diagnosis using multi-source and
multi-modal data is a hotspot and trend in research, which brings more possibilities for the …
multi-modal data is a hotspot and trend in research, which brings more possibilities for the …